README.md

---
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: codellama2-finetuned-codex-py
  results: []
datasets:
- iamtarun/python_code_instructions_18k_alpaca
language:
- en
pipeline_tag: text-generation
---



# codellama2-finetuned-codex-py

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Example Use Cases:
```
from transformers import AutoTokenizer
from transformers import pipeline
import torch

tokenizer = AutoTokenizer.from_pretrained("damerajee/codellama2-finetuned-alpaca-18k-fin")
pipe = pipeline(
    "text-generation",
    model="damerajee/codellama2-finetuned-alpaca-18k-fin",
    torch_dtype=torch.float16,
    device_map="auto",
)

text = "write a function that takes in print out each individual characters in a string"

sequences = pipe(
    text,
    do_sample=True,
    temperature=0.1,
    top_p=0.7,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=70,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")


```
 
## Training and evaluation data

| Step | Training Loss |
|------|---------------|
| 10   | 0.792200      |
| 20   | 0.416100      |
| 30   | 0.348600      |
| 40   | 0.323200      |
| 50   | 0.316300      |
| 60   | 0.317500      |
| 70   | 0.333600      |
| 80   | 0.329500      |
| 90   | 0.333400      |
| 100  | 0.309900      |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0